docs: def ght_alternative (img, template, indices):
      """
      Alternative implementation of the general hough transform, which uses iteration over
      indices rather than broadcasting rules like `ght`.
      
      It is therefore considerably slower, especially for large, multi-dimensional arrays.
      The only application are cases, where the hough transform should only be computed for
      a small number of points (=template centers) in the image. In this case the indices
      of interest can be provided as a list.
      
      Parameters
      ----------
      img : array_like
          The image in which to search for the structure.
      template : array_like
          A boolean array containing the structure to search for.
      indices : sequences
          A sequence of image indices at which to compute the hough transform.
      
      Returns
      -------
      hough_transform : ndarray
          The general hough transformation image.
      """
      # cast template to bool and img to numpy array
      img = numpy.asarray(img)
      template = numpy.asarray(template).astype(numpy.bool)
      
      # check supplied parameters
      if img.ndim != template.ndim:
          raise AttributeError('The supplied image and template must be of the same dimensionality.')
      if not numpy.all(numpy.greater_equal(img.shape, template.shape)):
          raise AttributeError('The supplied template is bigger than the image. This setting makes no sense for a hough transform.')
      
      # pad the original image
      img_padded = pad(img, footprint=template, mode='constant')
      
      # prepare the hough image
      if numpy.bool == img.dtype:
          img_hough = numpy.zeros(img.shape, numpy.int32)
      else:
          img_hough = numpy.zeros(img.shape, img.dtype)
          
      # iterate over the pixels, apply the template center to each of these and save the sum into the hough image
      for idx_hough in indices:
          idx_hough = tuple(idx_hough)
          slices_img_padded = [slice(idx_hough[i], None) for i in range(img_hough.ndim)]
          img_hough[idx_hough] = sum(img_padded[slices_img_padded][template])     
          
      return img_hough